Livestock and feedlot data collection and processing using UHF-band interrogation of radio frequency identification tags for feedlot arrival and risk assessment

ABSTRACT

An agricultural data collection framework is provided in a system and method for tracking and managing livestock, and for analyzing animal conditions such as health, growth, nutrition, and behavior. The framework uses ultra-high frequency interrogation of RFID tags to collect individual animal data across multiple geographical locations, and incorporates artificial intelligence techniques to develop machine learning base models for statistical process controls around each animal for evaluating the animal condition. The framework provides a determination of normality at an individual animal basis or for a specific location, and generates alerts, predictions, and a targeted processing or application schedule for prioritizing and delivering resources when intervention is needed.

CROSS-REFERENCE TO RELATED PATENT APPLICATION(S)

This patent application claims priority to, and is a continuation of,U.S. non-provisional application Ser. No. 17/364,510, filed on Jun. 30,2021, U.S. non-provisional application Ser. No. 16/852,826, filed onApr. 20, 2020 (now U.S. Pat. No. 11,055,633, issued on Jul. 6, 2021),and U.S. non-provisional application Ser. No. 16/569,503, filed on Sep.12, 2019 (now U.S. Pat. No. 10,628,756, issued on Apr. 21, 2020), thecontents of all of which are incorporated in their entirety herein. Inaccordance with 37 C.F.R. § 1.76, a claim of priority to each of theseapplications and patents is included in an Application Data Sheet filedconcurrently herewith.

FIELD OF THE INVENTION

The present invention relates to feedlot data collection and processing.Specifically, the present invention relates to data collection usingultra-high frequency interrogation of radio frequency identification(RFID) tags, and application of machine learning techniques to discernand predict animal health issues and other conditions relative togeographical regions, feedlots, pastures, pens, and other enclosures forlivestock.

BACKGROUND OF THE INVENTION

Existing technology for electronically tracking herds of livestocktypically involve storing data on radio-frequency identification tags,and using scanners to interrogate and obtain data from those tags.Present scanning techniques, however, have disadvantages that limit itsutility in collecting and processing livestock-related information. Forexample, scanning distance using low-frequency interrogation systems ison the order of centimeters, meaning that the interrogation devices mustbe in close proximity to the livestock and RFID tags from which data isto be collected. Further, low-frequency scanners can only scan one RFIDtag at a time, do not allow for simultaneous interrogation of multipletags in a single instance or sweep.

This has the practical limitation of limiting the data pipeline ofcollections over a large geographical area. Therefore, obtaining suchinformation and moving it into cloud-based storage paradigms is notcommon practice in the livestock management industry, because the issuesdescribed above severely impact the ability to perform advanced dataanalytics on livestock over wider geographical areas.

Another problem faced by the livestock industry is a limited ability toprocess data collected by interrogating radio-frequency identificationtags for large numbers of livestock over a wide geographical area, andanalyzing such information by region, by farm, by feedlot, by pasture,by pen, or by any other such metric. In other words, the combined natureof collecting data and analyzing livestock across a wide area means thatan application utilizing artificial intelligence techniques in a datamining process that folds RFID tag data with additional data sourcesrepresenting weather, markets, and other relevant information, islimited by the ability to interrogate tags and obtain data needed forsuch analytics.

Solutions to the problems above are key due to increased attention onfood security and traceability. Therefore, being able to track andprocess livestock in a combined approach that is able to quickly obtainand store data across wide distances and for multiple regions is helpfulfor many reasons, such as monitoring animal health, understanding andpromoting improvements in livestock growth and milk production, modelingfeed intake rate and inventory needs over the course of a growing seasonor feeding period, and enhancing food system sustainability.

There is therefore a need in the existing art for improvements incollecting livestock data over a wide geographical area and in theability to analyze livestock data attributes using such data, in anapproach that applies artificial intelligence techniques to predictivedata analytics and which combines RFID tag data with other data tobetter understand and manage the many issues attendant to maintaining alivestock population.

BRIEF SUMMARY OF THE INVENTION

The present invention is an agricultural data collection framework,provided in one or more systems and methods for evaluating conditions oflivestock across multiple geographical locations. The agricultural datacollection framework uses ultra-high frequency interrogation of RFIDtags to collect individual animal data across multiple regions, farms,feedlots, pastures, pens, and any other location or enclosure whereanimals are maintained, and incorporates artificial intelligencetechniques to develop machine learning base models for statisticalprocess controls around each animal for tracking and managing livestock,and for analyzing animal conditions such as health, growth, nutrition,and behavior.

Application of ultra-high frequency bulk reading of RFID tags enablesinterrogating multiple tags at the same time, and detection of a knowngrouping of objects such as livestock across multiple locations. Such aninterrogation paradigm enables process support for applying analytical,algorithmic tools to determining normality at an individual animal basisor for a specific location, and prioritizing and delivering resourceswhen intervention is needed in response to deviations from such anormality, due at least in part because of the greater range associatedwith reading RFID tags over ultra-high frequency bands. The use ofUHF-band interrogation addresses temporal issues with such a large-scalecollection approach, and enables advanced data analytics involvingapplications of artificial intelligence and machine learning in a datamining process that combines the collected livestock data withadditional, relevant data sources. Such a framework, it is to be noted,is not limited to livestock populations, but is usable in anyagricultural environment in which RFID tags are deployed to storeinformation.

It is one objective of the present invention to provide a system andmethod of large-scale collection of livestock data for evaluation ofanimal conditions. It is another objective of the present invention toprovide a system and method of applying advanced data analytics to sucha large-scale collection of data. It is yet another objective of thepresent invention to utilize ultra-high frequency interrogation of RFIDtags affixed to livestock for such a large-scale collection of data overmultiple regions, feedlots, farms, pastures, pens, or other enclosureswhere animals are maintained in multiple geographical locations.

It is another objective of the present invention to augment livestockdata obtained from such UHF-band interrogation of RFID tags with otherdata relative to the animal condition being evaluated, such asenvironmental data, nutrition data, regional data, animal-specific data,market data, and other producer-augmented or generated data. It is stillanother objective of the present invention to provide a framework fordata collection and analytics that includes a determination of normalityat an individual animal basis or for a specific location for animalconditions such as health, growth, nutrition, and behavior. It is yetanother objective of the present invention to generate alerts,predictions, and a targeted processing or application schedule forprioritizing and delivering resources when intervention is needed basedon such a determination of normality, and deviations therefrom.

Other objects, embodiments, features, and advantages of the presentinvention will become apparent from the following description of theembodiments, which illustrate, by way of example, principles of theinvention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate several embodiments of theinvention and together with the description, serve to explain theprinciples of the invention.

FIG. 1 is a system diagram illustrating components in an agriculturaldata collection and processing framework for analyzing data attributesin livestock tracking and management according to one embodiment of thepresent invention; and

FIG. 2 is a flowchart of steps in a process of performing anagricultural data collection and processing framework for analyzing dataattributes in livestock tracking and management according to oneembodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In the following description of the present invention, reference is madeto the exemplary embodiments illustrating the principles of the presentinvention and how it is practiced. Other embodiments will be utilized topractice the present invention and structural and functional changeswill be made thereto without departing from the scope of the presentinvention.

The present invention is, as noted above, an agricultural datacollection and processing framework 100, provided in one or more systemsand methods for utility in precision agriculture, and specifically forlivestock tracking and management. The agricultural data collection andprocessing framework 100 utilizes ultra-high frequency (UHF) bandinterrogation of RFID tags associated with livestock, and analyzeslivestock tracking and management characteristics in evaluating ananimal condition, and uses those characteristics to determine andpredict data attributes for an allocation and prioritization oflivestock-related resources over a wide geographical area forresponsiveness to animal conditions.

UHF scanning of RFID tags provides immediate advantages overlow-frequency alternatives. The scan distance is much greater, on theorder of meters rather than centimeters with low-frequency scanning.Also, UHF scanning allows for simultaneous scanning of multiple tags ina single sweep, whereas other technologies can only scan one tag at atime. Further, high frequency scanners are able to penetrate deeper, forexample through metals used for animal enclosures, increasing accuracyin the processing of data collected by being able to reach more RFIDtags for interrogation.

The agricultural data collection framework 100 contemplates that manydifferent modeling approaches may be applied in the present invention,and such approaches may also be referred to or described herein as anapplication of both statistical process controls for change detectionalgorithms, and artificial intelligence and machine learning, tocombined analytics involving the collection and processing of livestockdata. These different modeling approaches are used in the framework 100to determine a normality for a specific animal or for specific locationas it pertains to a modeled animal condition, and predicting orotherwise generating one or more outcomes using such a normalitydetermination. Regardless, the present invention enables improvedaccuracy in predicting data attributes that impact attributes in alivestock life cycle such as health, growth and milk production. Outputsfrom the framework, whether in the form of predictions, alerts, orotherwise, assist in allocating and prioritization usage of resourcesfor livestock tracking and management. Further, the present inventionallows producers of livestock to ensure that animals receive the diet,nutrition, health supplements, and veterinary care needed in response tosuch predictions and/or allocations and prioritizations.

FIG. 1 is a block diagram illustrating system components of theagricultural data collection and processing framework 100 for combinedanalytics in analyzing an animal condition 160, and determining andpredicting data attributes for livestock tracking and management over awide geographical area. The framework 100 applies a plurality of inputdata 110 to one or more mathematical processes within a multi-facetedmachine learning platform. These processes may include standardizedmodels, and may also include one or more models customized according toproprietary formulas. Regardless, the application of artificialintelligence and machine learning enables such mathematical processes tobe trained to identify data that is relevant to particular attributes ofan animal condition 160, and adjust outcomes accordingly. Further, theapplication artificial intelligence and machine learning may enable theframework 100 to select a particular or most appropriate model ormodels, or combinations thereof, for specific or desired outputs.Regardless, the framework 100 generates output data 170 that includespredictions 174, alerts 176, or other information relevant to livestocktracking and management 172, and may be configured to produce a widerange of information attendant to such livestock tracking and management172.

The data collection aspect of the framework 100 collects input data 110by interrogating radio-frequency identification (RFID) tags 104. Eachhead of livestock 102 has at least one RFID tag 104 coupled to it, whichstores relevant information about the animal to which it is coupled.RFID tags 104 are interrogated using ultra-high frequency (UHF) scannersor readers 150, which are part of a plurality of data processingcomponents 144 (not shown in FIG. 1 ) within a computing environment 140in which the systems and methods described herein are performed foranalytical processing, such as applying one or more process ormathematical models within a component(s) configured to develop machinelearning base models and 162 perform statistical process controls in oneor more change detection algorithms 152 on relevant input data 110. Thecomputing environment 140 may include one or more processors 142 and aplurality of software and hardware components, and the one or moreprocessors 142 and plurality of software and hardware components may beconfigured to execute program instructions or routines to perform thefunctions performed within the plurality of data processing components144.

It is to be understood that the plurality of data processing components144 are shown in FIG. 1 by their specific, respective reference numeralsas indicated below. It is to be further understood that these components144 are part of the larger computing environment 140, and constitute oneor more structures, hardware, firmware, or software, such as algorithms,routines, sub-routines, and the like, that are specifically configuredto execute particular functions within the agricultural data collectionand processing framework 100. It is to be additionally understood thatthe data processing components 144, and the respective elements of thepresent invention that together that comprise thesespecifically-configured components, may interchangeably be referred toas “components,” “modules,” “algorithms” (where appropriate), and anyother similar term that is intended to indicate an element for carryingout a specific data processing function.

The data processing components 144 also include a data retrieval andinitialization module 151, which is configured to ingest, receive,request, or otherwise obtain input data 110, whether it be frominterrogating RFID tags 104, or from additional sources as describedfurther herein. This data retrieval and initialization module 151 mayalso be configured to condition or format raw input data 110 from theRFID tags 104, and from such additional sources, so as to be preparedfor the artificial intelligence and machine learning 162 and statisticalprocess control and change detection algorithms 152 aspects of theframework 100.

In the agricultural data collection and processing framework 100 of thepresent invention, information obtained by the UHF readers 150 from theRFID tags 104 may also include geographical information 111, whichcorrelates the livestock information in a RFID tag 104 with locationdata. Data about livestock 102 may therefore be geo-tagged withinformation identifying a region 112, a feedlot 113, a pen 114, a farm115, or any other type of enclosure or location where livestock 102 aremaintained. Geographical location data 111 may be correlated with GlobalPositioning System (GPS) and tracking data for enhancement of the inputdata 110, and therefore a RFID tag 104 may include one or more GPS datapoints representative of the region 112, feedlot 113, pen 114, or farm115 in which the tag 104 is located. The framework 100 may thereforeutilize components such as a GPS-enabled receiver in conjunction withUHF readers 150 to detects signals relative to the geographical locationto compute the tag's precise position on Earth using the one or more GPSdata points. The GPS-enabled receiver may thereby extract and determinethe geographical location of the tag 104 from the GPS data points.

UHF interrogation of RFID tags 104 may be initiated by the dataretrieval or initialization component 151, or may occur automaticallyand independently thereof. Regardless, data obtained as a result of thisUHF interrogation is then transferred and stored by the data retrievaland initialization module 151 for further processing as discussed below.

The data retrieval and initialization module 151 is also configured toingest, receive, request, or otherwise obtain additional informationthat aids the framework 100 in processing the input data 110 collectedfrom RFID tags 104, by augmenting livestock data and geographical data111 with other data that is relevant to evaluating, modeling anddiagnosing an animal condition 160. This additional information mayinclude environmental data 117, regional data 120, nutrition data 123,regional animal-specific or model-specific data 124, producer-augmenteddata 129, and reader attributes 133, and regardless of its type, mayinclude any information not temporally gathered directly or on site,such as for example market pricing (such as livestock commodities datafor live cattle, feeder cattle, corn, and milk future prices), diseaseoutbreaks in other geographies, etc. It should be noted however that insome cases this additional information may be stored on the RFID tags104, regardless of the time or place it was gathered or generated.

Environmental data 117 includes both ambient climatological ormeteorological information relative to where livestock 102 aremaintained or where a RFID tag 104 resides, as well as spatial and othernon-weather physical conditions. For example, environmental data mayinclude weather and climate information 118, such as temperature,precipitation, humidity, barometric pressure and other weather-relatedcharacteristics for the area or location where the RFID tag 104 residesor the livestock 102 is maintained. Weather and climate information mayalso include short and long term weather predictions and forecasts forthat same area or location. Environmental data 117 may also, assuggested above, be parsed by location 119, and may indicate a type andsize of pen or enclosure (for example, an indication that livestock arekept in a barn or freestall, and the size of each), field and pastureconditions (for example, USDA Drought Monitor), and available grazingvegetation, where the livestock 102 are maintained.

Regional data 120 may further include trend and diagnosis informationfor the region where a RFID tag 104 resides, or where livestock 102 aremaintained. Such trend and diagnosis information may provide healthinformation and forecasts for the livestock by region which may impact,growth and behavior going forward, and which may influence growth anddairy production modeling. For example, regional data 120 may indicatethat respiratory treatments within a particular data collection regionare up 30% for the present quarter, or that a diagnosis of foot rot isexpected to be 15% higher in the next quarter due to higher regionalprecipitation the last 30 days.

Nutritional information 123 may provide feed and forage data for aparticular region or animal. For example, nutritional information mayindicate that a predominant feed type specifically consists of somepercentage of dry matter, or may more broadly provide a breakdown offeed nutrient percentages over time. Nutritional data 123 may alsoprovide the mathematical formula by which weight gain allowance fromenergy intake is analyzed, such as for example in a net energy gainmodel or net energy required for maintenance model. Nutritional data 123may also provide the mathematical formula by which milk production fromenergy intake is analyzed, such as for example in a net energy lactationmodel. Nutritional data 123 may also indicate what supplements orpharmaceuticals have been provided as part of a feed mix, and when.

Model or animal-specific data 124 includes information that identifies,and is particular to, an animal or group of animals, and which enablesan arrival or risk assessment that can serve as a starting point whereproducer has entered all known information on the animal, before anyprocessing data or additional decisions are made. Specific examples ofthis arrival or risk assessment information in model-specific data 124may include an origin 125, a changing value such as its current weightor age 126, a gender or breed 127, and a DNA or lineage 128. It may alsoinclude information such as purchase weight and location, as well as adistance traveled, weaning status, vaccination status, shrink (payweight less arrival weight), and other information that enables a robustrisk assessment where a series of decision tree questions are utilizedto categorize a health risk that influences other processing protocols.The arrival and risk assessment may therefore provide the agriculturaldata collection framework 100 with a complete animal health history. Itshould be noted that this arrival and risk assessment data may beprocured from many sources, such as directly from an RFID tag 104itself, from a reference database maintained or stored separately, orprovided by third party sources such as another user of the framework100 or from a third party or separate system integrated with theframework 100.

The processing of input data 110 in the framework 100 may be furtheraugmented with producer-augmented information 129 that may include manydifferent types of data. The producer-augmented information 129 mayinclude RFID tag-related data 130, such as for example an identificationof correlated events relative to livestock 102. RFID tag-related data130 may also indicate events such as new RFID tags 104 being added tothe geographical location being monitored, events such as RFID tags 104being removed from the geographical location due to tag defect ordestruction or animal death, events that represent a replacement of aRFID tag 104 or assumption of a previous history with a new tag 104, andevents indicative of tag breakage or multiple tags present on the sameanimal.

Other producer-augmented information 129 may include feed-related data131 such as feed delivery properties. Such properties may include a timeof delivery, a composition of a ration, an amount of feed delivery (andan amount of each component of a ration delivered at a particular time,and a bunk score. Still other producer-augmented information 129 mayinclude a geographical topology 132 representing the location in which atag 104 is located. This may include a region size and other details ofa coverage region, such as terrain characteristics, a presence andlocation of available water, field boundaries, and other relevantinformation.

Additional producer-augmented information 129 may include managementinformation such as vaccination and treatment history, productiontechnology use or sorting history. Further health-related managementinformation may include confirmed diagnoses, confirmed recovery fromillness, treatments used to address diagnoses and illnesses, etc.

The processing of input data 110 in the framework 100 may also beaugmented with reader attributes 133. These attributes 133 may includeabsolute or relative reader location details, antenna power settings,date and time attributes, and a tag RSSI (Receive Signal StrengthIndicator). Ambient conditions sensed around the tag 104 may also beincluded, such as temperature and moisture, and as noted below, sensorsand other hardware may be utilized in conjunction with tags 104 toprovide information about such ambient conditions.

Input data 110 may be further augmented in another embodiment of thepresent invention using hardware devices that are associated with orproximate to RFID tags 104. For example, an inclinometer may be utilizedto measure an angle of inclination of livestock at various times of aday, for example when presumed to be feeding, to further and moreaccurately evaluate characteristics such as head down duration andeating rate, as well as to more accurately determine feeding andnon-feeding times and intervals where univariate or multi-variate modelsof such characteristics are applied. It is therefore to be understoodthat the present invention may incorporate input data 110 that come fromnot only from third party sources, but also sensors and other hardwaredevices than may be utilized in conjunction with livestock 102.

Regardless of the type of input data 110 that is ingested to augmentdata from RFID tags 104, the data retrieval and initialization component151 provides the information to an artificial intelligence engine, whichis configured to develop one or more machine learning base models 162 ofone or more characteristics impacting the animal condition 160. The oneor more machine learning base models 162 include algorithms thatidentify additional information from the input data 110 and obtain suchadditional information from one or more sources as noted above.

The machine learning base models 162 then assign weights 164 to theinput data 110. These weights 164 represent biases in the input data 110relative to the animal condition 160, and may be assigned based onmultiple variables or factors, such as for example a prior response orresponses to an animal condition 160 being modeled, either in the formof a specific treatment or within a geographical location similar tothat within which the animal condition 160 is being modeled. Regardless,the weights 164 are aggregated to generate a weighted vector of learningdata 166 that is provided to the statistical process controls in thechange detection algorithms performed by the component 152.

The statistical process controls and change detection algorithms 152apply one or more mathematical processes to the output of the machinelearning base models 162 to evaluate the animal condition 160 andgenerate a corresponding profile. These mathematical processes areapplied to perform change detection, at least by identifying trackingand management characteristics 172 of the livestock 102 and a normality156 of the animal condition 160. These mathematical processes at leastinclude a statistical analysis 153, a sequential analysis 154, and acumulative summation (CUSUM model) 155. Regardless of the mathematicalprocess or model used to evaluate the input data 110 and the weightedvector of learning data 166, they may be derived from existing,standardized models, and may also include models that are customized toincorporate unique characteristics based upon the input data 110discussed above and the specific animal condition 160 being profiled.

The resultant profile of the animal condition 160 is then applied acrossthe multiple geographical locations in which the one or more animals arelocated to determine a normality 156 relative to a specific animal inthe one or more animals, and identify differences in the animalcondition 160 for a specific geographical area. The framework 100 istherefore configured to develop statistical process controls and performchange detection analyses around each animal for a determination ofnormal at an individual animal basis, so that opportunities forintervention where the artificial intelligence engine identifiesdeviations from such normality determinations can be quickly performed.The present invention may therefore be understood to be, in one aspectthereof, a framework 100 for evaluating animal health that tries toidentify healthy animals and healthy conditions rather than sick orunhealthy ones, so that conditions outside of normal parameters can beclassified as such and diagnosis, treatment, and prevention proceedsfrom there as a starting point.

The profile of the animal condition 160, and livestock tracking andmanagement characteristics 172 therein, may be generated as output data170 as discussed further below, and may also be provided back to themachine learning base models 162 and used to adjust and/or train a basemodel 168. The framework 100 therefore “learns” from outcomes of thestatistical process control and change detection algorithms 152 toimprove the weights and correlations 164 assigned to the input data 110,and the corresponding weighted vector of learning data 166 for eachanimal condition 160 modeled. Therefore, the framework 100 incorporatesa feedback loop in the form of adjustments to the base model 168 thatenables validation of the statistical process control and changedetection algorithms 152 and the predictions 174 and alerts 176generated therefrom as output data 170.

The output data 170 includes, as noted above, predictions 174 and alerts176 that are the result of livestock tracking and managementcharacteristics 172 in the profile of the animal condition 160. Thelivestock tracking and management characteristics 172, predictions 174and alerts 176 may be provided to users via a display, such as agraphical user interface, interactive or otherwise, for example via asupport tool or other mechanism.

Many manifestations of the livestock tracking and managementcharacteristics 172, predictions 174 and alerts 176 in the output data170 are contemplated and within the scope of the present invention. Inone aspect of the present invention, the output data 170 may be used todevelop and application schedule 180 for delivery of a response in anintervention to deviations from the normality 156 as discussed above,and to allocate and prioritize resource usage 181 for such a response.Output data 170 may also include specific information derived from thelivestock tracking and management characteristics 172, predictions 174and alerts 176, such as for example a pre-diagnosis of health issues182, identification of disease trends 183, peak livestock weights 184,behavioral patterns 185 (for example, grazing behavior suggestive ofinadequate pasture), and indications of specific health events 186, suchas calving 187, estrus 188, and injury 189. The output data 170 mayfurther be processed to identify environmental interactions 190 thataffect other livestock models, such as growth models and dairyproduction models.

Many other services and outcomes are possible, and may be providedeither by directly by the framework 100 itself, or through one or moreapplication programming interfaces (APIs). For example, the framework100 may include modules configured to generate predictions 174 andalerts 176 to marketing organizations interested in when cattle willcome of weight in the future, manufacturers of particular feedcomponents interested in when medicine, additives and supplements shouldbe re-ordered, nutritionists and veterinary visits scheduled, and buyersor auctioneers of livestock notified. It is to be understood that manytypes of predictions 174 and alerts 176 are possible within the presentinvention, and it is not to be limited to any one type of prediction 174or alert 176 mentioned herein. The present invention may, as suggestedabove, also enable one or more additional and specific APIs to provideparticular information or services and generate specific outcomes fromthe output data 170 and the livestock tracking and managementcharacteristics 172, predictions 174 and alerts 176 that are generatedfrom the framework 100.

In one example where the framework 100 of the present invention may beapplied, UHF readers of scanners 150 are deployed across a feedlot 113that includes one or more pens 114, alleys, loading/unloading areas, andother places where livestock 102 may be located. Deployment locationsfor the UHF readers/scanners 150 may include all regions of the feedlot113, so that no areas are explicitly excluded. This includes areas with“attractants” such as water and feed sources, as well as areas without(for example, holding pens may be provided with water or food). Theenables input data 110 for animals in geographic areas marked as beingwithout attractants during feeding times to also be data points ofinterest for the machine learning base models 162.

RFID tags 104 are read in real time by the UHF reader devices 150. Inputdata 110 collected from the tags 104 may be provided to an aggregatedstorage mechanism, such as a relational database, along with readerattributes 133 and any other relevant data points collected from thefeedlot 113. Such input data 110 may be directly presented to theaggregated storage mechanism from the readers 150 using network,cellular, Wi-Fi, Bluetooth, or other comparable communications network.Alternatively, input data 110 may be presented to the aggregated storagemechanism through the use of data pass-through devices, which aredevices which collect data from the readers 150 and act as a liaison topass the data to the aggregated storage mechanism. Examples ofpass-through devices include tablets, cellular devices, a point(“smart”) scale head or other device capable of collecting data from thereaders 150 and passing information to the storage mechanism using an IPnetwork such as Wi-Fi, or serial communications protocol such asBluetooth, NFC (near-field communication), or the like. Pass-throughdevices may include “smart” phones or other computing devices, and maybe transitory devices mounted to trucks, tractors, other agriculturalimplements, manned or unmanned, as well as to manned or unmanned flightvehicles. Regardless, in such an example input data 110 is pooled andcombined with optional on-demand sources of additional information forthe artificial intelligence engine in the one or more machine learningbase models 162, and for the statistical process controls and changedetection algorithms 152.

In an exemplary approach, the input data 110 for evaluating a particularanimal condition 160 may include DNA (genetic history) and lineage(origin) of livestock 128, and any treatment histories derivedtherefrom; water tank data (such as frequency and duration of waterconsumption), feed bunk data (such as frequency and duration of feedration consumption). The one or more machine learning base models 162takes these inputs develops correlations and weights 164 to generate theweighted vector space of learning data 166 based on any actual,historical producer-specified treatments provided for the animalcondition 160. This learning process is followed by a real-timepredictive analysis performed by the statistical process controls andchange detection algorithms 152 to identify livestock tracking andmanagement characteristics 172 where there is a possible deviation froma normality 156 for a particular geographical location (such as thefeedlot 113) to identify sick livestock before they show any visualsigns of stress or illness or otherwise become in need of treatment. Theset of input data 110 may be further augmented with data outside of theproducer's feedlot 113, such as with nutritional ration data, weatherdata, treatments at other locations in a supply chain, for examplecow/calf, backgrounder or stocker operations, or a calf ranch or heiferraiser, brands of treatments (generic versus commercial), etc.

The learning data based on the actual producer treatment data is used inreal time to predict animals with behaviors that would also lead toproducer treatments of the current livestock 102. These animals would beidentified for the producer to do a “pre-check” health determination,allowing the producer to possibly prevent further outbreak or animaldeath.

FIG. 2 is a flowchart illustrating a process 200 for performing theframework 100 of the present invention. The process 200 begins at step210 by interrogating RFID tags 104 as noted above with readers 150utilizing a high-frequency communications band (UHF) to begin theprocessing of onboarding input data 110 relative to evaluating an animalcondition 160. This information is initially processed to determine whatadditional information may be requested and obtained to perform thevarious processing steps for evaluating the animal condition 160 at step220.

Detailed processing of the input data 110 in the framework 100 thenbegins at step 230, with the development of machine learning base models162 in the artificial intelligence component of the present invention.The models 162 evaluate the input data 110 and additional informationfor the animal condition 160 at issue (or for a particular geographicallocation) and identify biases and correlations between one or morevariables which are used to assign weights 164, at step 240. Theseweighted 164 variables are used to compile a vector space of weightedlearning data 166.

At step 250, the weighted vector data set 166 is applied to changedetection algorithms 152 for statistical process, using as noted aboveone or more mathematical processes to identify a deviation fromnormality 156 for the animal condition 160. At step 260, the framework100 and process 200 generate a profile of the animal condition 160 andtracking and management characteristics 172 relative the animalcondition 160. The process then filters and identifies data attributesfor a particular animal condition 160 and for one or more geographicallocations at step 270, and in one aspect of the present inventiongenerates a targeted application schedule 180 of resources based on theprofile to address the animal condition 160 at step 280. As noted above,this may include an allocation and prioritization of resources, and mayfurther be present on a display for a user or producer to take furtherspecific action.

Returning to FIG. 1 , as noted above, the framework 100 for developinglivestock tracking and management characteristics 172 for analyzing ananimal condition 160 is a multi-faceted approach that performs, in oneaspect thereof, different mathematical processes for evaluating changedetection to determine a normality 156 and predict any deviationstherefrom. These mathematical processes include a statistical analysis153, a sequential analysis 154 (a specific type of statisticalanalysis), and a cumulative sum analysis (a specific type of sequentialanalysis). The selection of the process to be utilized depends on thetype of animal condition 160 being modeled, and on the types of inputdata 110. And, as noted above, a particular process may be customizeddepending on similar characteristics (the type of animal condition 160,and the type of input data 110).

For example, the present invention may evaluate an animal condition 160such as the effectiveness and accuracy of monitoring feeding behaviorpatterns, which may be utilized to predict the onset of health issuessuch as bovine respiratory disease in beef cattle. The framework 100 mayapply one or more cumulative summation (CUSUM) models 155 that are eachconfigured to evaluate univariate traits as they pertain to feeding,such as for example bunk visit frequency, bunk visit duration, head downduration, eating rate, time to bunk, and non-feeding intervals, or anyother feeding-related characteristic. It is to be understood that thesecharacteristics may be obtained or derived from input data 110, such asproducer-augmented information 129, within the framework 100, and maynot necessarily be obtained directly from RFID tags 104.

Outcomes from these models may be used to construct multivariate factorsthat are also monitored using CUSUM. From these constructs, accuracy maybe selected based on the weighted vector of learning data 166 for themost pertinent and accurate predictive analysis. In this manner, astatistical process control can be implemented for evaluating changedetection within the framework 100 for an appropriate allocation andprioritization of resources to address animal conditions 160.

Other uses of the output data 170 in the present invention are alsopossible, and within the scope of the present invention. In oneembodiment, the output data 170 may be used to address specificity andsensitivity tolerances. In one example, the framework 100 may be used tomatch an operator labor resources or animal illness risk by adjustingoutput sensitivity and specificity to minimize candidate animalsidentified for treatment. In another example, the output data 170specificity and sensitivity may be adjusted to inform a greater numberof candidate animals due to high risk periods as determinedenvironmental 117 or regional data 120 trends.

It is to be understood that the word “livestock” in the presentinvention may refer to any type of livestock 102 for which tracking andmanagement characteristics 172 in analysis of an animal condition 160may be developed, and the scope of this disclosure is not to be limitedto any one specific type of livestock 102 referred to herein, nor is itlikewise to be limited to one condition for any one type of livestockreferred to herein. Livestock 102 may therefore include, but not belimited in any way to, beef cattle, dairy cattle, hogs, poultry, sheep,goats, bison, horses, etc. The present invention is therefore applicableto all types of livestock 102, and the modeling approach discussedherein may be adjusted depending on the type of livestock 102 beingmodeled.

The systems and methods of the present invention may be implemented inmany different computing environments 140. For example, the statisticalprocess control and change detection algorithms 152 may be implementedin conjunction with a special purpose computer, a programmedmicroprocessor or microcontroller and peripheral integrated circuitelement(s), an ASIC or other integrated circuit, a digital signalprocessor, electronic or logic circuitry such as discrete elementcircuit, a programmable logic device or gate array such as a PLD, PLA,FPGA, PAL, and any comparable means. In general, any means ofimplementing the methodology illustrated herein can be used to implementthe various aspects of the present invention. Exemplary hardware thatcan be used for the present invention includes computers, handhelddevices, telephones (e.g., cellular, Internet enabled, digital, analog,hybrids, and others), and other such hardware. Some of these devicesinclude processors (e.g., a single or multiple microprocessors), memory,nonvolatile storage, input devices, and output devices. Furthermore,alternative software implementations including, but not limited to,distributed processing, parallel processing, or virtual machineprocessing can also be configured to perform the methods describedherein.

The systems and methods of the present invention may also be partiallyimplemented in software that can be stored on a storage medium, executedon programmed general-purpose computer with the cooperation of acontroller and memory, a special purpose computer, a microprocessor, orthe like. In these instances, the systems and methods of this inventioncan be implemented as a program embedded on personal computer such as anapplet, JAVA® or CGI script, as a resource residing on a server orcomputer workstation, as a routine embedded in a dedicated measurementsystem, system component, or the like. The system can also beimplemented by physically incorporating the system and/or method into asoftware and/or hardware system.

Additionally, the data processing functions disclosed herein may beperformed by one or more program instructions stored in or executed bysuch memory, and further may be performed by one or more modulesconfigured to carry out those program instructions. Modules are intendedto refer to any known or later developed hardware, software, firmware,artificial intelligence, fuzzy logic, expert system or combination ofhardware and software that is capable of performing the data processingfunctionality described herein.

The foregoing descriptions of embodiments of the present invention havebeen presented for the purposes of illustration and description. It isnot intended to be exhaustive or to limit the invention to the preciseforms disclosed. Accordingly, many alterations, modifications andvariations are possible in light of the above teachings, may be made bythose having ordinary skill in the art without departing from the spiritand scope of the invention. For example, the input data 110 may beaugmented with data collected hardware devices in association with orproximate to a RFID tag, such as an inclinometer. It is thereforeintended that the scope of the invention be limited not by this detaileddescription. For example, notwithstanding the fact that the elements ofa claim are set forth below in a certain combination, it must beexpressly understood that the invention includes other combinations offewer, more or different elements, which are disclosed in above evenwhen not initially claimed in such combinations.

The words used in this specification to describe the invention and itsvarious embodiments are to be understood not only in the sense of theircommonly defined meanings, but to include by special definition in thisspecification structure, material or acts beyond the scope of thecommonly defined meanings. Thus if an element can be understood in thecontext of this specification as including more than one meaning, thenits use in a claim must be understood as being generic to all possiblemeanings supported by the specification and by the word itself.

The definitions of the words or elements of the following claims are,therefore, defined in this specification to include not only thecombination of elements which are literally set forth, but allequivalent structure, material or acts for performing substantially thesame function in substantially the same way to obtain substantially thesame result. In this sense it is therefore contemplated that anequivalent substitution of two or more elements may be made for any oneof the elements in the claims below or that a single element may besubstituted for two or more elements in a claim. Although elements maybe described above as acting in certain combinations and even initiallyclaimed as such, it is to be expressly understood that one or moreelements from a claimed combination can in some cases be excised fromthe combination and that the claimed combination may be directed to asub-combination or variation of a sub-combination.

Insubstantial changes from the claimed subject matter as viewed by aperson with ordinary skill in the art, now known or later devised, areexpressly contemplated as being equivalently within the scope of theclaims. Therefore, obvious substitutions now or later known to one withordinary skill in the art are defined to be within the scope of thedefined elements.

The claims are thus to be understood to include what is specificallyillustrated and described above, what is conceptually equivalent, whatcan be obviously substituted and also what essentially incorporates theessential idea of the invention.

The invention claimed is:
 1. A method, comprising: interrogating aplurality of radio-frequency identification (RFID) tags using at leastone reader configured to communicate with the plurality ofradio-frequency identification tags over an ultra-high frequency band tocollect input data that includes data identifying one or more animals towhich each RFID tag is coupled in at least one feedlot; processing theinput data within a computing environment in which a plurality of dataprocessing modules are executed in conjunction with at least oneprocessor, the plurality of data processing modules configured toanalyze an animal condition at the at least one feedlot in initialarrival protocols, by developing a machine learning base model of one ormore characteristics impacting the animal condition, by identifying andaugmenting the input data with additional animal-related informationrelative to the animal condition that includes ration nutritionalinformation, and treatments provided to the one or more animals at otherlocations, where the one or more animals originate from a common lineageor from a cow-calf operation, integrating meteorological informationinto the machine learning base model by identifying historical andcurrent weather information at the at least one feedlot and at the otherlocations, correlating the historical and current weather informationwith the input data and the additional animal-related information, andaugmenting the machine learning base model with weather forecasts at theat least one feedlot to identifying common conditions in themeteorological information affecting the animal condition, assigningweights to the input data, the additional animal-related information,and the meteorological information relative to one or more priorresponses to the animal condition to generate a weighted vector oflearning data, applying the weighted vector of learning data to one ormore statistical process controls that simulate the animal condition atthe at least one feedlot, and identifying specific health riskcharacteristics impacting the animal condition to categorize a healthrisk for the one or more animals at the at least one feedlot andlater-arriving animals at the least one feedlot over time; and modifyingthe initial arrival protocols at the at least one feedlot for the one ormore animals to address the health risk, and for each later-arrivinganimal exhibiting the same or similar health risk characteristics uponarrival at the at least one feedlot.
 2. The method of claim 1, furthercomprising predicting a health change for the one or more animals at theat least one feedlot and each later-arriving animal at the least onefeedlot over a feeding period, and identifying one or more temporaldecision points over the feeding period for addressing the predictedhealth change, based on the health risk.
 3. The method of claim 2,wherein the predicted health change includes one or more of a peakweight, a drop in weight that exceeds a pre-determined amount, anincrease in weight that exceeds a pre-determined amount, a livestockillness, and an identification of a trend indicative of a diseasespread.
 4. The method of claim 2, further comprising generating at leastone of a growth model or a dairy production model for the one or moreanimals at the at least one feedlot and each later-arriving animal atthe least one feedlot, and adjusting the at least one of a growth modelor a dairy production model in response to the predicted health change.5. The method of claim 2, further comprising generating a targetedapplication schedule for an allocation of resources to address thepredicted health change at the one or more temporal decision points overthe feeding period.
 6. The method of claim 1, wherein the additionalanimal-related information relative to the animal condition furtherincludes treatment-specific information that includes one or more of abrand of treatments, a treatment dosage, a treatment timing, and acorrelation of the treatment timing to the meteorological information.7. The method of claim 1, wherein the additional animal-relatedinformation relative to the animal condition further includes observedanimal health changes at one or more of the at the at least one feedlot,the other locations, and additional feedlots across multiplegeographical locations, the observed animal health changes including oneor more of a change in an animal health condition, a change in an animaldietary or nutrition condition, a change in an animal growth condition,and a change in an animal behavior condition.
 8. The method of claim 1,wherein the ration nutritional information further includes one or moreof an ingredient composition, a diet, a ration amount, a feedinghistory, types of forage available to the one or more animals, and waterdata.
 9. A system, comprising: a radio frequency identification taginterrogation network configured to interrogate a plurality ofradio-frequency identification (RFID) tags using at least one readerconfigured to communicate with the plurality of RFID tags over anultra-high frequency band and collect input data that includes dataidentifying one or more animals to which each RFID tag is coupled in atleast one feedlot; and an artificial intelligence engine configured tomodel the input data by analyzing an animal condition upon arrival atthe at least one feedlot in initial arrival protocols, by developing amachine learning base model of one or more characteristics impacting theanimal condition, the machine learning base model configured to identifyand augment the input data with additional animal-related informationrelative to the animal condition that includes ration nutritionalinformation, and treatments provided to the one or more animals at otherlocations, where the one or more animals originate from a common lineageor from a cow-calf operation, integrate meteorological information intothe machine learning base model by identifying historical and currentweather information at the at least one feedlot and at the otherlocations, correlating the historical and current weather informationwith the input data and the additional animal-related information, andaugmenting the machine learning base model with weather forecasts at theat least one feedlot to identifying common conditions in themeteorological information affecting the animal condition, assignweights to the input data, the additional animal-related information,and the meteorological information relative to one or more priorresponses to the animal condition to generate a weighted vector oflearning data, apply the weighted vector of learning data to one or morestatistical process controls that simulate the animal condition at theat least one feedlot, and identify specific health risk characteristicsimpacting the animal condition to categorize a health risk for the oneor more animals at the at least one feedlot and later-arriving animalsat the least one feedlot over time, wherein the initial arrivalprotocols at the at least one feedlot are modified for the one or moreanimals to address the health risk, and for each later-arriving animalexhibiting the same or similar health risk characteristics upon arrivalat the at least one feedlot.
 10. The system of claim 9, wherein themachine learning base model is further configured to predict a healthchange for the one or more animals at the at least one feedlot and eachlater-arriving animal at the least one feedlot over a feeding period,and identify one or more temporal decision points over the feedingperiod for addressing the predicted health change, based on the healthrisk.
 11. The system of claim 10, wherein the predicted health changeincludes one or more of a peak weight, a drop in weight that exceeds apre-determined amount, an increase in weight that exceeds apre-determined amount, a livestock illness, and an identification of atrend indicative of a disease spread.
 12. The system of claim 10,wherein at least one of a growth model or a dairy production model aredeveloped for the one or more animals at the at least one feedlot andeach later-arriving animal at the least one feedlot, and wherein the atleast one of a growth model or a dairy production model are adjusted inresponse to the predicted health change.
 13. The system of claim 10,wherein the machine learning base model is further configured to producea targeted application schedule for an allocation of resources toaddress the predicted health change at the one or more temporal decisionpoints over the feeding period.
 14. The system of claim 9, wherein theadditional animal-related information relative to the animal conditionfurther includes treatment-specific information that includes one ormore of a brand of treatments, a treatment dosage, a treatment timing,and a correlation of the treatment timing to the meteorologicalinformation.
 15. The system of claim 9, wherein the additionalanimal-related information relative to the animal condition furtherincludes observed animal health changes at one or more of the at the atleast one feedlot, the other locations, and additional feedlots acrossmultiple geographical locations, the observed animal health changesincluding one or more of a change in an animal health condition, achange in an animal dietary or nutrition condition, a change in ananimal growth condition, and a change in an animal behavior condition.16. The system of claim 9, wherein the ration nutritional informationfurther includes one or more of an ingredient composition, a diet, aration amount, a feeding history, types of forage available to the oneor more animals, and water data.
 17. A method of assessing livestockhealth for animals arriving at a feedlot, comprising: instantiating amachine learning base model of one or more characteristics impacting ananimal condition upon arrival of one or more animals to at least onefeedlot in initial arrival protocols, by obtaining input data byinterrogating a plurality of radio-frequency identification (RFID) tagsusing at least one reader configured to communicate with the pluralityof radio-frequency identification tags over an ultra-high frequencyband, the input data identifying one or more animals to which each RFIDtag is coupled in at least one feedlot, identifying and augmenting theinput data with additional animal-related information relative to theanimal condition that includes ration nutritional information, andtreatments provided to the one or more animals at other locations, wherethe one or more animals originate from a common lineage or from acow-calf operation, integrating meteorological information, byidentifying historical and current weather information at the at leastone feedlot and at the other locations, correlate the historical andcurrent weather information with the input data and the additionalanimal-related information, and augment the machine learning base modelwith weather forecasts at the at least one feedlot to identifying commonconditions in the meteorological information affecting the animalcondition, assigning weights to the input data, the additionalanimal-related information, and the meteorological information relativeto one or more prior responses to the animal condition to generate aweighted vector of learning data, applying the weighted vector oflearning data to one or more statistical process controls that simulatethe animal condition at the at least one feedlot, and identifyingspecific health risk characteristics impacting the animal condition tocategorize a health risk for the one or more animals at the at least onefeedlot and later-arriving animals at the least one feedlot over time;and modifying the initial arrival protocols at the at least one feedlotfor the one or more animals to address the health risk, and for eachlater-arriving animal exhibiting the same or similar health riskcharacteristics upon arrival at the at least one feedlot.
 18. The methodof claim 17, further comprising predicting a health change for the oneor more animals at the at least one feedlot and each later-arrivinganimal at the least one feedlot over a feeding period, and identifyingone or more temporal decision points over the feeding period foraddressing the predicted health change, based on the health risk. 19.The method of claim 18, wherein the predicted health change includes oneor more of a peak weight, a drop in weight that exceeds a pre-determinedamount, an increase in weight that exceeds a pre-determined amount, alivestock illness, and an identification of a trend indicative of adisease spread.
 20. The method of claim 18, further comprisinggenerating at least one of a growth model or a dairy production modelfor the one or more animals at the at least one feedlot and eachlater-arriving animal at the least one feedlot, and adjusting the atleast one of a growth model or a dairy production model in response tothe predicted health change.
 21. The method of claim 18, furthercomprising generating a targeted application schedule for an allocationof resources to address the predicted health change at the one or moretemporal decision points over the feeding period.
 22. The method ofclaim 17, wherein the additional animal-related information relative tothe animal condition further includes treatment-specific informationthat includes one or more of a brand of treatments, a treatment dosage,a treatment timing, and a correlation of the treatment timing to themeteorological information.
 23. The method of claim 17, wherein theadditional animal-related information relative to the animal conditionfurther includes observed animal health changes at one or more of the atthe at least one feedlot, the other locations, and additional feedlotsacross multiple geographical locations, the observed animal healthchanges including one or more of a change in an animal health condition,a change in an animal dietary or nutrition condition, a change in ananimal growth condition, and a change in an animal behavior condition.24. The method of claim 17, wherein the ration nutritional informationfurther includes one or more of an ingredient composition, a diet, aration amount, a feeding history, types of forage available to the oneor more animals, and water data.